Independent component regression (ICR) is an efficient method for tackling non-Gaussian problems. In this work, the defects of the conventional ICR are analyzed, and a novel quality-relevant independent component regression (QR-ICR) method based on distance covariance and distance correlation is proposed. QR-ICR extracts independent components (ICs) using a quality-relevant independent component analysis (QR-ICA) algorithm, which simultaneously maximizes the non-Gaussianity of ICs and statistical dependency between ICs and quality variables. Meanwhile, two new types of statistical criteria, called cumulative percent relevance (CPR) and Max-Dependency (Max-Dep), are proposed to rank the order and determine the number of ICs according to their contributions to quality variables. The proposed QR-ICR_(CPR) and QR-ICR_(Max-Dep) methods were validated through a vinyl acetate monomer production process and a benchmark near-infrared spectral data. The results have demonstrated that the proposed QR-ICR_(CPR) and QR-ICR_(Max-Dep) provide simpler predictive models and give better prediction performances than PLS, ICR, ICR_(CPR), and ICR_(Max-Dep).

独立分量回归（ICR）是解决非高斯问题的有效方法。在这项工作中，分析了常规ICR的缺陷，并提出了一种基于距离协方差和距离相关性的质量相关的独立分量回归（QR-ICR）新方法。QR-ICR使用质量相关的独立分量分析（QR-ICA）算法提取独立分量（IC），该算法可同时最大化IC的非高斯性以及IC与质量变量之间的统计依赖性。同时，提出了两种新的统计标准，称为累积百分比相关性（CPR）和最大相关性（Max-Dep），以对顺序进行排名并根据IC对质量变量的贡献来确定IC的数量。拟议的QR-ICR _（CPR）通过乙酸乙烯酯单体生产工艺和基准近红外光谱数据验证了QR和ICR _{（Max-Dep）}方法。结果表明，与PLS，ICR，ICR _（CPR）和ICR _{（Max-Dep）}相比，拟议的QR-ICR _（CPR）和QR-ICR _{（Max-Dep）}提供了更简单的预测模型，并提供了更好的预测性能。